Original Article Risk association of polycystic ovary …Risk association of polycystic ovary...

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Int J Clin Exp Med 2016;9(4):7088-7101 www.ijcem.com /ISSN:1940-5901/IJCEM0018469 Original Article Risk association of polycystic ovary syndrome with paraoxonase 1 and TNF-α gene polymorphisms: a meta-analysis Liu Liu, Jing Yang, Qian Liu, Wenjie Yan, Yanqi Wen Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan 430060, China Received October 25, 2015; Accepted January 21, 2016; Epub April 15, 2016; Published April 30, 2016 Abstract: Nowadays, the association of the single nucleotide polymorphisms (SNPs) in the paraoxonase 1 (PON-1) and Tumor Necrosis Factor alpha (TNF-α) genes with polycystic ovary syndrome (PCOS) risk has been receiving a lot of attention and an extensive of work have done for it. However, for these published results, they were inconsistent and sometime even contradictory with each other . To address this issue, this meta-analysis aims to provide a com- prehensive summary and analysis on the association between these polymorphisms and PCOS risk. A systematic review and analysis for the eligible studies published before June 1, 2015 was conducted based on the materials retrieving from database of PubMed, Embase, Web of Science, Medline, Chinese National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM) and Wan Fang online library. According to heterogeneity test results, odds ratios (OR) with corresponding 95% confidence intervals (95% CI) were used to evaluate the strength of the association under a fixed or random effect model. Funnel plots and Egger’s tests were performed to test for possible publication bias. All statistical analyses were performed using the STATA 12.0 statistical software. Nineteen case-control studies were covered in this meta-analysis with a total of 3,838 cases and 3,321 controls, where the following polymorphisms are involved: PON-1 (-108C/T, -192Q/R, -55L/M), TNF-α (-308G/A, -1031T/C). Our results show a significant association between PCOS and PON-1 (-108C/T) polymorphism (For T allele vs. C allele: OR=1.17, 95% CI=1.04-1.32, P=0.012. For TT vs. CC: OR=1.437, 95% CI=1.43-1.84, P=0.005. For TT vs. TC+CC: OR=1.51, 95% CI=1.21-1.87, P=0.011), PON-1 (-192Q/R) polymorphism (For R allele vs. Q allele: OR=1.61, 95% CI=1.08- 2.42, P=0.02. For RR+RQ vs. QQ: OR=1.02, 95% CI=1.02-2.68, P=0.043) and TNF-α (-1031 T/C) polymorphism (For CC+CT vs. TT: OR=1.97, 95% CI=1.56-2.50, P<0.001. For CC vs. TT: OR=2.11, 95% CI=1.09-4.06, P=0.026). No associations are found between PCOS risk and PON-1 (-55L/M), TNF-α (-308G/A) polymorphism. Therefore, Variant T allele of PON-1 (-108C/T), variant R allele of PON-1 (-192Q/R), and variant C allele of TNF-α (1031T/C) may be associated with a higher PCOS susceptibility, and SNPs of PON-1 (-55L/M), TNF-α (-308G/A) may be not related to PCOS risk. Keywords: Paraoxonase 1, tumor necrosis factor alpha, polycystic ovary syndrome, gene polymorphism, meta- analysis Introduction Polycystic ovary syndrome (PCOS), a common cause of reproductive dysfunction and abnor- mal glucose metabolism, is a heterogeneous reproductive endocrine and metabolic disorder occurred in 5-7% women of childbearing age [1-3]. Characterized by oligomenorrhea or ame- norrhea, anovulation, hyperandrogenism and polycystic ovarian morphology, PCOS can result in abnormal hormone environment which incr- eases the risk of some other diseases such as type 2 diabetes, cardiovascular disease and en- dometrial cancer [4, 5]. Till now, the underlying etiologies and pathogenesis of PCOS are not well understood. A popular idea is that genetic actor plays the key role where the interaction between multiple genetic and environmental factors may participate in its development and progression [6, 7], and the nucleotides muta- tions or single nucleotide polymorphisms (SN- Ps) contributing to the mysterious and complex genetic background may be involved. In recent years, candidate genes for etiology of PCOS have been hot issue which was proposed as important contributors to PCOS in hyperandro-

Transcript of Original Article Risk association of polycystic ovary …Risk association of polycystic ovary...

Page 1: Original Article Risk association of polycystic ovary …Risk association of polycystic ovary syndrome with paraoxonase 1 and TNF-α gene polymorphisms: a meta-analysis Liu Liu, Jing

Int J Clin Exp Med 2016;9(4):7088-7101www.ijcem.com /ISSN:1940-5901/IJCEM0018469

Original Article Risk association of polycystic ovary syndrome with paraoxonase 1 and TNF-α gene polymorphisms: a meta-analysis

Liu Liu, Jing Yang, Qian Liu, Wenjie Yan, Yanqi Wen

Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan 430060, China

Received October 25, 2015; Accepted January 21, 2016; Epub April 15, 2016; Published April 30, 2016

Abstract: Nowadays, the association of the single nucleotide polymorphisms (SNPs) in the paraoxonase 1 (PON-1) and Tumor Necrosis Factor alpha (TNF-α) genes with polycystic ovary syndrome (PCOS) risk has been receiving a lot of attention and an extensive of work have done for it. However, for these published results, they were inconsistent and sometime even contradictory with each other. To address this issue, this meta-analysis aims to provide a com-prehensive summary and analysis on the association between these polymorphisms and PCOS risk. A systematic review and analysis for the eligible studies published before June 1, 2015 was conducted based on the materials retrieving from database of PubMed, Embase, Web of Science, Medline, Chinese National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM) and Wan Fang online library. According to heterogeneity test results, odds ratios (OR) with corresponding 95% confidence intervals (95% CI) were used to evaluate the strength of the association under a fixed or random effect model. Funnel plots and Egger’s tests were performed to test for possible publication bias. All statistical analyses were performed using the STATA 12.0 statistical software. Nineteen case-control studies were covered in this meta-analysis with a total of 3,838 cases and 3,321 controls, where the following polymorphisms are involved: PON-1 (-108C/T, -192Q/R, -55L/M), TNF-α (-308G/A, -1031T/C). Our results show a significant association between PCOS and PON-1 (-108C/T) polymorphism (For T allele vs. C allele: OR=1.17, 95% CI=1.04-1.32, P=0.012. For TT vs. CC: OR=1.437, 95% CI=1.43-1.84, P=0.005. For TT vs. TC+CC: OR=1.51, 95% CI=1.21-1.87, P=0.011), PON-1 (-192Q/R) polymorphism (For R allele vs. Q allele: OR=1.61, 95% CI=1.08-2.42, P=0.02. For RR+RQ vs. QQ: OR=1.02, 95% CI=1.02-2.68, P=0.043) and TNF-α (-1031 T/C) polymorphism (For CC+CT vs. TT: OR=1.97, 95% CI=1.56-2.50, P<0.001. For CC vs. TT: OR=2.11, 95% CI=1.09-4.06, P=0.026). No associations are found between PCOS risk and PON-1 (-55L/M), TNF-α (-308G/A) polymorphism. Therefore, Variant T allele of PON-1 (-108C/T), variant R allele of PON-1 (-192Q/R), and variant C allele of TNF-α (1031T/C) may be associated with a higher PCOS susceptibility, and SNPs of PON-1 (-55L/M), TNF-α (-308G/A) may be not related to PCOS risk.

Keywords: Paraoxonase 1, tumor necrosis factor alpha, polycystic ovary syndrome, gene polymorphism, meta-analysis

Introduction

Polycystic ovary syndrome (PCOS), a common cause of reproductive dysfunction and abnor-mal glucose metabolism, is a heterogeneous reproductive endocrine and metabolic disorder occurred in 5-7% women of childbearing age [1-3]. Characterized by oligomenorrhea or ame- norrhea, anovulation, hyperandrogenism and polycystic ovarian morphology, PCOS can result in abnormal hormone environment which incr- eases the risk of some other diseases such as type 2 diabetes, cardiovascular disease and en-

dometrial cancer [4, 5]. Till now, the underlying etiologies and pathogenesis of PCOS are not well understood. A popular idea is that genetic actor plays the key role where the interaction between multiple genetic and environmental factors may participate in its development and progression [6, 7], and the nucleotides muta-tions or single nucleotide polymorphisms (SN- Ps) contributing to the mysterious and complex genetic background may be involved. In recent years, candidate genes for etiology of PCOS have been hot issue which was proposed as important contributors to PCOS in hyperandro-

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ported that some of polymorphisms of TNF-α are related to gynecological diseases, e.g. pre-eclampsia and endometriosis [25]. It has been shown that single nucleotide polymorphism (SNP) at positions -308G/A (rs1800629) [G (guanine) >A (adenine)] and -1031T/C (rs17999- 64) [T (thymine) >C (cytosine)] of TNF-α gene is linked with altered promoter activity so as to differentiate plasma levels of TNF-α [26]. The rs1800629 polymorphism of TNF-α gene has been reported to produce the less common TNF2 allele which was identified to be associ-ated with insulin dependent diabetes and increasing adiposity [27, 28]. Further, the rs179- 9964 polymorphism of TNF-α gene has been proposed to be a risk haplotype for type 2 dia-betes and associated with several inflammato-ry disorders and PCOS [29, 30].

Based on the above mentioned studies, the purpose of this paper was to examine the effect of gene PON-1 -108C/T, -192Q/R, -55L/M poly-morphism and gene TNF-α -308 G/A, -1031T/C polymorphism on human PCOS. Although the association of gene PON-1 -108C/T, -192Q/R, -55L/M polymorphisms and gene TNF-α -308- G/A, -1031T/C polymorphisms with PCOS risk has been widely studied, the results are incon-sistent and controversial due to the factors of limited sample sizes and differences in the source of controls. Inspired by the above men-tioned issues in the current research, a meta-analysis of all eligible studies was conducted here, with the ultimate goal of elucidating a comprehensive and reliable conclusion by me- ans of pooling available data and reevaluating the relationship between the PCOS incidence risk and the above gene polymorphisms.

Materials and methods

Literature search strategy

A systematic literature search for eligible stud-ies published before June 1, 2015 was per-formed among the following electronic data-bases: PubMed, Embase, Web of Science, Med- line, Chinese National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM) and Wanfang online libraries. The search strategy of following terms was used: (“PCOS” or “polycystic ovary syndrome”) and (“PON-1” or “paraoxonase 1” or “tumor necrosis factor al- pha” or “TNF-alpha”) and (“polymorphism” or “variant” or “mutation”). The publication langua-

genism related genes, insulin action related genes, chronic inflammatory factor genes and so on [8].

PON-1

Enzyme paraoxonase 1 (PON-1) is a calcium-dependent multifunctional enzyme mainly ex- pressed by the liver, which is associated with high density lipoproteins (HDLP) and impeded atherogenic modifications (oxidation) of low (LDLP) and ultimately decreased the risk of car-diovascular disease [9, 10]. The human PON-1 gene is located on 7q21.3 chromosome con-taining 9 exons spanning 26 kb [11]. PON-1 activity depends on both its environmental fact and polymorphic variants such as polymorphic forms at positions C108T, A192G, L55M. The promoter region PON-1 -108C/T (rs705379) polymorphism [C (cytosine) >T (thymine)] was reported to influence the expression of PON-1 enzyme and what’s more, the coding region PON-1 -192Q/R (rs662) polymorphism [A (ade-nine) >G (guanine)] and PON-1 -55L/M (rs854- 560) polymorphism [T (thymine) >A (adenine)] was reported to influence the concentration and activity of PON-1 enzyme [12-15]. Reduced PON-1 activity was associated with disorders such as cardiovascular disease, unfavorable lipid profiles, and diabetes [16-18]. What is more, several polymorphisms PON-1 gene have been recognized to be associated with PCOS [19-21]. However, relationship between PON-1 -108C/T, -192Q/R, -55L/M polymorphism and PCOS is still controversial and ambiguous due to the heterogeneous condition with the com-plex genetic backgrounds and environmental exposures.

TNF-α

Tumor Necrosis Factor alpha (TNF-α) is a proin-flammatory cytokine secreted by the activated monocytes and macrophages or some fat cells. TNF-α can also be expressed in granulosa cells, follicular fluid and lutein cells. Rice et al report-ed that TNF-α is capable of inhibiting follicle stimulating hormone (FSH)-induced oestradiol secretion in small follicles from the human ovary and stimulating in-vitro proliferation of androgen producing theca cells [22, 23]. The human TNF-α gene is located on the short arm of chromosome 6 (6p21.3) [24]. Its expression is regulated at both the transcriptional and post-transcriptional levels. Several studies re-

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ge was restricted to English or Chinese and the publication species was restricted to human subjects. The reference lists of retrieved arti-cles were also manually screened to identify additional potential sources. This meta-analy-

sis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [31] with only slight modification to better suit the nature of those studies.

Figure 1. Flow diagram of the study selection process.

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Inclusion criteria

A study was included only if it met all the follow-ing criteria: (1) clinical case-control or cohort stu- dies focused on the relationship between PON- 1 (-108C/T, -192Q/R, -55L/M), TNF-α (-308G/A, -1031T/C) polymorphisms and susceptibility to PCOS; (2) all patients met the diagnostic cri-teria for PCOS, the National Institute of Health (NIH) criteria or the Rotterdam criteria or clini-cal criteria; (3) sufficient genotype data in cases and controls to calculate odds ratios (OR) and 95% confidence intervals (95% CI) was provid-

ed. Studies were excluded if they did not con-firm to all the inclusion criteria.

Data extraction

According to the PRISMA guidelines, two review-ers independently checked the articles and extracted the following information from each study: surname of fist author, published year, origin of country, ethnicity, sample size, single nucleotide polymorphisms (SNPs), genotype nu- mber of cases and controls, genotyping meth-od, and PCOS diagnostic criteria. A consensus

Table 1. Main characteristics of all eligible studies

Author Year Country Racial descent

Sample size Controlsource

Genotyping method

Diagnostic stan-dardCase Control

PON-1 -108C/T (rs705379)

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Ferk [39] 2014 Slovenia Caucasian 118 108 HB PCR-RFLP Rotterdam criteria

Paltoglou [38] 2013 Greece Caucasian 142 112 HB PCR-RFLP NIH criteria

Zhang [37] 2011 China Asian 346 315 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Clinical criteria

PON-1 -192Q/R (rs662)

Roshan Dadachanji [42] 2015 India Asian 482 326 HB PCR-RFLP Rotterdam criteria

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Paltoglou [38] 2013 Greece Caucasian 142 112 HB PCR-RFLP NIH criteria

Wang [41] 2012 China Asian 610 503 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Clinical criteria

PON-1 -55L/M (rs854560)

Roshan Dadachanji [42] 2015 India Asian 482 326 HB PCR-RFLP Rotterdam criteria

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Wang [41] 2012 China Asian 610 503 HB PCR-RFLP Rotterdam criteria

Lenarcik [20] 2010 Poland Caucasian 130 70 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Rotterdam criteria

TNF-α -308G/A (rs1800629)

Grech [43] 2014 Greece Caucasian 185 165 HB PCR-RFLP Rotterdam criteria

Wen [45] 2013 China Asian 144 72 HB PCR-RFLP Rotterdam criteria

Deepika [44] 2013 India Asian 283 306 HB ARMS-PCR Rotterdam criteria

Peng [47] 2010 China Asian 130 175 HB PCR-RFLP Rotterdam criteria

Vural [48] 2010 Turkey Caucasian 97 95 HB PCR-RFLP Rotterdam criteria

Zhang [46] 2010 China Asian 78 40 HB Microarray Rotterdam criteria

Mao [49] 2000 China Asian 118 54 HB PCR-RFLP NIH criteria

Milner [50] 1999 Australia Caucasian 84 28 HB PCR-SSCP NIH criteria

TNF-α -1031T/C (rs1799964)

Hazwanie [51] 2014 Malaysia Asian 12 145 HB ARMS-PCR Rotterdam criteria

Deepika [44] 2013 India Asian 283 306 HB ARMS-PCR Rotterdam criteria

Wang [52] 2013 China Asian 135 180 HB PCR-RFLP Clinical criteria

Yun [30] 2011 Korea Asian 217 144 HB PCR-RFLP Rotterdam criteriaNotes: PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; HB: hospital based; ARMS-PCR: amplification refractory mutation system; PCR-SSCP: Polymerase chain reaction-single strand conformation polymorphism. NIH: National Institute of Health.

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between reviewers was reached after discus-sion for disagreements.

Statistical analysis

Analysis of polymorphisms was conducted in at least three studies. The Cochran Q test and I2

test were used to evaluate the potential between-study heterogeneity. The significant heterogeneity was indicated by P<0.05 for the Q test or I2>50% [32]. If P value >0.05 or I2<50%, a fixed-effect model was used to esti-mate pooled odds ratios (OR) and their corre-sponding 95% confidence intervals (CI) [33].

Table 1. Main characteristics of all eligible studies

Author Year Country Racial descent

Sample size Controlsource

Genotyping method

Diagnostic stan-dardCase Control

PON-1 -108C/T (rs705379)

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Ferk [39] 2014 Slovenia Caucasian 118 108 HB PCR-RFLP Rotterdam criteria

Paltoglou [38] 2013 Greece Caucasian 142 112 HB PCR-RFLP NIH criteria

Zhang [37] 2011 China Asian 346 315 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Clinical criteria

PON-1 -192Q/R (rs662)

Roshan Dadachanji [42] 2015 India Asian 482 326 HB PCR-RFLP Rotterdam criteria

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Paltoglou [38] 2013 Greece Caucasian 142 112 HB PCR-RFLP NIH criteria

Wang [41] 2012 China Asian 610 503 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Clinical criteria

PON-1 -55L/M (rs854560)

Roshan Dadachanji [42] 2015 India Asian 482 326 HB PCR-RFLP Rotterdam criteria

Zhang [40] 2015 China Asian 455 441 HB PCR-RFLP Rotterdam criteria

Wang [41] 2012 China Asian 610 503 HB PCR-RFLP Rotterdam criteria

Lenarcik [20] 2010 Poland Caucasian 130 70 HB PCR-RFLP Rotterdam criteria

San Millán [36] 2004 Spain Caucasian 72 42 HB PCR-RFLP Rotterdam criteria

TNF-α -308G/A (rs1800629)

Grech [43] 2014 Greece Caucasian 185 165 HB PCR-RFLP Rotterdam criteria

Wen [45] 2013 China Asian 144 72 HB PCR-RFLP Rotterdam criteria

Deepika [44] 2013 India Asian 283 306 HB ARMS-PCR Rotterdam criteria

Peng [47] 2010 China Asian 130 175 HB PCR-RFLP Rotterdam criteria

Vural [48] 2010 Turkey Caucasian 97 95 HB PCR-RFLP Rotterdam criteria

Zhang [46] 2010 China Asian 78 40 HB Microarray Rotterdam criteria

Mao [49] 2000 China Asian 118 54 HB PCR-RFLP NIH criteria

Milner [50] 1999 Australia Caucasian 84 28 HB PCR-SSCP NIH criteria

TNF-α -1031T/C (rs1799964)

Hazwanie [51] 2014 Malaysia Asian 12 145 HB ARMS-PCR Rotterdam criteria

Deepika [44] 2013 India Asian 283 306 HB ARMS-PCR Rotterdam criteria

Wang [52] 2013 China Asian 135 180 HB PCR-RFLP Clinical criteria

Yun [30] 2011 Korea Asian 217 144 HB PCR-RFLP Rotterdam criteriaNotes: PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; HB: hospital based; ARMS-PCR: amplification refractory mutation system; PCR-SSCP: Polymerase chain reaction-single strand conformation polymorphism. NIH: National Institute of Health.

Table 2. Meta-analysis results for the PON-1 (-308C/T, -192Q/L, -55L/M), TNF-α (-308G/A, -1031T/C) Polymorphism and PCOS risk

Contrast Population N CASE CONTROLTest of association

ModeTest of heterogeneity

OR 95% CI P P I2 (%)PON-1 -108C/T (rs705379)

T vs. C Total 5 2266 2036 1.17 [1.04, 1.32] 0.012 F 0.550 0.0 TT vs. CC Total 5 600 483 1.43 [1.43, 1.84] 0.005 F 0.450 0.0 TT vs. TC+CC Total 5 1133 1018 1.51 [1.21, 1.87] 0.011 R 0.012 69.0 TT+TC vs. CC Total 5 1133 1018 1.06 [0.88, 1.27] 0.547 F 0.158 39.5

PON-1 -192Q/R (rs662)

R vs. Q Total 5 3522 2848 1.61 [1.08, 2.42] 0.020 R <0.001 91.7 RR vs. QQ Total 5 996 770 1.86 [1.49, 2.32] 0.075 R 0.007 71.9 RR vs. RQ+QQ Total 5 1761 1424 1.41 [0.98, 2.02] 0.061 R 0.016 67.3 RR+RQ vs. QQ Total 5 1761 1421 1.65 [1.02, 2.68] 0.043 R <0.001 84.1

PON-1 -55L/M (rs854560)

M vs. L Total 5 3498 2764 0.92 [0.77, 1.10] 0.364 F 0.309 16.5 MM vs. LL Total 5 1483 1167 1.25 [0.72, 2.17] 0.432 F 0.881 0.0 MM vs. ML+LL Total 5 1749 1382 1.32 [0.78, 2.21] 0.299 F 0.915 0.0 MM+ML vs. LL Total 5 1749 1382 0.86 [0.70, 1.05] 0.140 F 0.335 12.3

TNF-α -308G/A (rs1800629)

A vs. G Total 8 2153 1844 0.98 [0.82, 1.16] 0.786 F 0.267 20.5 Caucasians 3 732 576 1.34 [0.90, 1.98] 0.148 F 0.549 0.0

Asian 5 1421 1268 0.90 [0.74, 1.10] 0.297 F 0.327 13.6 AA vs. GG Total 8 675 541 0.59 [0.26, 1.35] 0.210 F 0.599 0.0

Caucasians 3 296 251 0.90 [0.26, 3.16] 0.875 F 0.740 0.0 Asian 5 379 290 0.42 [0.13, 1.31] 0.134 F 0.293 18.6

AA vs. AG+GG Total 8 1078 922 0.62 [0.28, 1.38] 0.238 F 0.575 0.0 Caucasians 3 366 288 0.88 [0.25, 3.05] 0.835 F 0.724 0.0

Asian 5 712 634 0.48 [0.16, 1.40] 0.180 F 0.244 29.1 AA+AG vs. GG Total 8 1078 922 1.01 [0.76, 1.33] 0.954 F 0.32 14.0

Caucasians 3 366 288 1.41 [0.92, 2.17] 0.112 F 0.478 0.0 Asian 5 712 634 0.77 [0.53, 1.12] 0.177 F 0.632 0.0

TNF-α -1031T/C (rs1799964)

C vs. T Caucasians - - - - - - - - -Asian 4 1294 1550 1.40 [0.89, 2.23] 0.149 R 0.003 78.4

CC vs. TT Caucasians - - - - - - - - -Asian 4 341 508 2.11 [1.09, 4.06] 0.026 F 0.373 4.0

CC vs. CT+TT Caucasians - - - - - - - - -Asian 4 647 775 1.03 [0.32, 3.39] 0.956 R 0.027 67.2

CC+CT vs. TT Caucasians - - - - - - - - -Asian 4 647 775 1.97 [1.56, 2.50] <0.001 F 0.405 0.0

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Otherwise, a random effect model was applied. The Z test was used to estimate the statistical

significance of pooled ORs and P-value <0.05 was considered statistically significant. Sensi-

Figure 2. Forest plots for the association between the PON-1 -108C/T polymorphism and PCOS risk in the total popu-lations. The T allele increased the risk of PCOS. A. T vs. C allele model; B. TT vs. CC model; C. TT vs. TC+CC model.

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Results

A total of 265 relevant papers were identified using the pre-specified search strategy. In accordance with the inclusion criteria, 19 case-control studies were included [20, 30, 36-52], of which 8 were on PON-1 [5 were on -108 CT (rs705379), 5 were on -192Q/R (rs662), 5 were on -55L/M (rs854560)] and 11 were on TNF-α

tivity analyses were performed to assess the stability of the results by removing one study at a time. Potential publication bias was analyzed by Begg’s funnel plot and Egger’s test [34, 35]. All two tailed P<0.05 were considered as statis-tical significance. All above statistical analyses were performed using STATA statistical soft-ware (version 12.0; Stata Corp, College Station, TX, USA).

Figure 3. Forest plots for the association between the PON-1 -192Q/R polymorphism and PCOS risk in the total populations. The R allele increased the risk of PCOS. A. R vs. Q allele model; B. RR+RQ vs. QQ model.

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acteristics and methodology of the included studies are summarized in Table 1.

Association between the PON-1 (-108C/T, -192Q/R, -55L/M) polymorphism and PCOS risk

The evaluation of the association between the PON-1 -108C/T (rs705379, C>T), -192Q/R (rs-

[8 were on -308G/A (rs1800629), 4 were on -1031T/C (rs1799964)]. A flow chart of the selec- tion process and the specific reasons for excluding from this meta-analysis are shown in Figure 1. A total number of 7,159 individual were included in this meta-analysis, of which 3,838 were PCOS patients and 3,321 were controls. The publication years of included studies ranged from 1999 to 2015. The char-

Figure 4. Forest plots for the association between the TNF-α -1031T/C polymorphism and PCOS risk in the total populations. The C allele increased the risk of PCOS. A. CC vs. TT allele model; B. CC+CT vs. TT model.

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95% CI=0.77-1.10, P=0.364; MM vs. LL: OR= 1.25, 95% CI=0.72-2.17, P=0.432; MM vs. ML+ LL: OR=1.32, 95% CI=0.78-2.21, P=0.299; MM +ML vs. LL: OR=0.86, 95% CI=0.70-1.05).

Association between the TNF-α (-308G/A, -1031T/C) polymorphism and PCOS risk

The evaluation of the association between the TNF-α -308G/A (rs1800629, G>A), TNF-α -1031- T/C (rs1799964, T>C) polymorphism and PCOS incidence is summarized in Table 2.

662, A>G), -55L/M (rs854560, T>A) polymor-phism and PCOS incidence risk is summarized in Table 2.

5 case control studies [36-40] with a total of 1133 PCOS cases and 1018 controls were assessed to identify associations between the PON-1 (-108C/T) polymorphism and PCOS. The results indicated that the PON-1 (-108C/T) poly-morphism was associated with PCOS in the allelic model (T vs. C: OR=1.17, 95% CI=1.04-1.32, P=0.012), model (TT vs. CC: OR=1.437, 95% CI=1.43-1.84, P=0.005) and recessive

Figure 5. Begg’s funnel plot analysis was used to detect publication bias for the association between PON-1 -108C/T polymorphism and PCOS risk in T vs. C allele model.

Figure 6. Begg’s funnel plot analysis was used to detect publication bias for the association between PON-1 -192Q/R polymorphism and PCOS risk in R vs. Q allele model.

model (TT vs. TC+CC: OR= 1.51, 95% CI=1.21-1.87, P= 0.011). However, no obvio- us associations were found in dominant model (TT+TC vs. CC: OR=1.06, 95% CI= 0.88-1.27, P=0.547). A for-est plot of the PON-1 (-108- C/T) polymorphism is sho- wn in Figure 2.

For the PON-1 (-192Q/R) po- lymorphism, the data from five studies (PCOS 1761 ca- ses and 1424 controls) [36, 38, 40-42] were combined and significant associations were found in the allelic mo- del (R vs. Q: OR=1.61, 95% CI=1.08-2.42, P=0.02) and dominant model (RR+RQ vs. QQ: OR=1.02, 95% CI= 1.02-2.68, P=0.043). How- ever, in the recessive mo- del (RR vs. RQ+QQ: OR= 1.41, 95% CI=0.98-2.02, P =0.061) and model (RR vs. QQ: OR=1.86, 95% CI=1.49-2.32, P=0.075) no associa-tion was detected. A forest plot of the PON-1 (-192Q/R) polymorphism is shown in Figure 3.

Under all genetic models, no obvious associations we- re found between the PON- 1 (-55L/M) polymorphism and PCOS risk when five stu- dies (1749 cases and 1382 controls) [20, 36, 40-42] we- re pooled into the meta-analysis (M vs. L: OR=0.92,

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for the meta-analysis. The statistical results did not show any evidence of publication bias (P>0.05), with the exception of the PON-1 (-108 C/T) polymorphism under the allelic model with the Begg’s test and Egger’s test (P=0.027 and P=0.041, respectively; as shown in Table 3). To evaluate the possibility of publication bias, sen-sitivity analysis was performed by the trim and fill method while unchanged data throughout the filled meta-analysis was found, suggesting the absence of publication bias for the PON-1 (-108C/T) polymorphism under the allelic mo- del.

Discussion

It is well known that PCOS is a common endo-crine disease, associated with dyslipidemia, vascular endothelial injury, oxidative stress, chronic inflammation and other cardiovascular disease risk factors. The occurrence of poly-morphisms in the TNF-α and PON-1 genes in women affected by PCOS has also been inves-tigated over the past few decades [30, 44]. How- ever, no clear consensus has been reached. To our best knowledge, this is the first meta-analy-sis on PON-1 (-108C/T, -192Q/R, -55L/M), TNF-α (-308G/A, -1031T/C) polymorphisms and PC- OS risk.

In the current meta-analysis, it shows no signifi-cant association between PON-1 (-55L/M) and PCOS risk in the overall population analysis under all genetic models. However, the findings

8 studies (1119 cases and 935 controls) [43-50] investigated the association of the TNF-α (-308G/A) polymorphism with PCOS risk were included in the analysis. The Fixed effect model was conducted. However, no association was found between the TNF-α (-308G/A) polymor-phism and PCOS risk in the overall population (A vs. G: OR=0.98, 95% CI=0.82-1.16, P=0.267; AA vs. GG: OR=0.59, 95% CI=0.26-1.35, P= 0.210; AA vs. GA+GG: OR=0.62, 95% CI=0.28-1.38, P=0.238; AA+GA vs. GG: OR=1.01, 95% CI=0.76-1.33, P=0.954). A stratified analysis was conducted to assess the effect estimated in subgroups defined by ethnicity, however, still no obvious associations were found.

Additionally, 4 studies [30, 44, 51, 52] (647 ca- ses and 775 controls) provided data between the TNF-α (-1031T/C) polymorphism with PCOS. The results indicated that the TNF-α (-1031T/C) polymorphism was associated with PCOS in the dominant model (CC+CT vs. TT: OR=1.97, 95% CI=1.56-2.50, P<0.001) and model (CC vs. TT: OR=2.11, 95% CI=1.09-4.06, P=0.026). How- ever, no obvious associations were found in allelic model (C vs. T: OR=1.4, 95% CI=0.89-2.23, P=0.149) and recessive model (CC vs. CT+TT: OR=1.03, 95% CI=1.09-4.06, P=0.026), as shown in Figure 4. Unfortunately, all of the studies were conducted for Asian and no study from Caucasians was retrieved and analyzed. Therefore, there was no direct evidence show-ing association between TNF-α (-1031T/C) poly-morphism and PCOS incidence in Caucasians.

Figure 7. Begg’s funnel plot analysis was used to detect publication bias for the association between TNF-α -1031T/C polymorphism and PCOS risk in CC vs. TT allele model.

Sensitivity analyses and publication bias

Sensitivity analysis was co- nducted to test the influ-ence of each single study to the pooled OR by ran-domly removing any indi-vidual study. The analytical result of PON-1 (-108C/T, -192Q/R, -55L/M) and TNF- α (-308G/A, -1031T/C) ge- ne polymorphism was not significantly altered. Beg- g’s funnel test and Egger’s linear test (Figures 5-7, Su- pplementary Figures 1, 2 and 3) were used to assess the publication bias throu- ghout the studies selected

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tion. What’s more, variant R allele of PON-1 (-192Q/R) was also found being as- sociated with a higher risk of PCOS in allelic model (R vs. Q) and dominant model (RR+RQ vs. QQ). However, no association was found in the dominant model (TT +TC vs. CC) of PON-1 (-108- C/T), model (RR vs. QQ) and recessive model (RR vs. RQ+QQ) of PON-1 (-192- Q/R), which could probably results from the insuffici- ent sample sizes. PCOS is associated with increased oxidative stress and sys-temic inflammation [53, 54]. What is more, Lower PON1 activity was obser- ved in patients with famil-ial hypercholesterolemia or diabetes [36, 55]. The -108- C→T and -192Q→R vari-ants of PON-1 greatly influ-ences the expression, pro-tein level or activities of la- ctonase PON-1, which pla- ys an important role in pre-venting oxidative stress and controlling inflamma-tion, and the absolute or relative lack of PON-1 lac-tonase activity may con-tribute to the PCOS pathol-ogy [40, 56, 57]. Therefore, our results give a valuable hint that by detecting the PON-1 (-108C/T) and PON- 1 (-192Q/R) polymorphi- sm, the forecasting the PC- OS susceptibility can be ac- hieved.

What is more, a strong as- sociation was identified wi- th the TNF-α -1031T>C po- lymorphism, while no sig-

Table 3. P values for publication bias testsContrast Population Begg’s test (P) Egger’s test (P)

PON-1 -108C/T (rs705379)

T vs. C Total 0.027 0.041TT vs. CC Total 0.086 0.008TT vs. TC+CC Total 0.462 0.051TT+TC vs. CC Total 1 0.663

PON-1 -192Q/R (rs662)

R vs. Q Total 0.806 0.407RR vs. QQ Total 1 0.484RR vs. RQ+QQ Total 1 0.567RR+RQ vs. QQ Total 0.462 0.485

PON-1 -55L/M (rs854560)

M vs. L Total 0.806 0.73MM vs. LL Total 1 0.552MM vs. ML+LL Total 0.296 0.415MM+ML vs. LL Total 1 0.667

TNF-α -308G/A (rs1800629)

A vs. G Total 0.657 0.753Caucasians 1 0.948

Asian 0.806 0.543AA vs. GG Total 0.712 0.65

Caucasians 1 0.836Asian 1 0.652

AA vs. AG+GG Total 0.712 0.705Caucasians 1 0.806

Asian 1 0.58AA+AG vs. GG Total 0.375 0.18

Caucasians 1 0.707Asian 0.221 0.098

TNF-α -1031T/C (rs1799964)

C vs. T Asian 1 0.595Caucasians - -

CC vs. TT Asian 0.734 0.532Caucasians - -

CC vs. CT+TT Asian 0.734 0.786Caucasians - -

CC+CT vs. TT Asian 0.734 0.747Caucasians - -

from the current meta-analysis supported that variant T allele of PON-1 (-108C/T) was associ-ated with a higher risk of PCOS. The effect on PCOS susceptibility was found in the allelic model (T vs. C), model (TT vs. CC) and recessive model (TT vs. TC+CC), confirming their associa-

nificant association was found between the -308G>A polymorphism and PCOS risk in the overall population or subgroup analysis under all genetic models. The data showed that vari-ant C allele of TNF-α (-1031T/C) was associat-ed with a higher risk of PCOS in the model (CC

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In this current meta-analysis, there are some limitations and shortages. First of all, the num-ber of study and the sample sizes were not large enough for each gene polymorphism, which may not have sufficient statistic power to assess the differences. Secondly, all eligible studies were limited to English and Chinese papers and a language bias could exist. What is more, most included articles were online pub-lished studies, and the studies that were unpublished online so far might not cover in this research, which may also result in publica-tion bias. Finally, some potential confounding factors were not well controlled for the multi-factorial etiology of PCOS such as BMI, lifestyle, alcohol consumption and so on, where the roles of above mentioned status should be evaluated in PCOS development.

In conclusion, the present meta-analysis indi-cated that PON-1 (-108C/T, -192Q/R) and TNF- α (-1031T/C) polymorphism could be a potential risk factor contributing to PCOS, from which a potential application is proposed: by taking these genes polymorphisms as useful biomark-ers, the PCOS susceptibility of human being could be forecasted in this way. However, the conclusion should be interpreted with caution due to the above mentioned limitations. In our future research, larger-scale studies associat-ed with multiple ethnicities and well-matched controls are to be implemented to confirm our findings further.

Disclosure of conflict of interest

None.

Address correspondence to: Dr. Jing Yang, Repro- ductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan 430060, China. E-mail: [email protected]

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Supplementary Figure 1. Begg’s funnel plot analysis was used to detect publication bias for the association be-tween PON-1 -108C/T polymorphism and PCOS risk in TT vs. CC allele model (A) and TT vs. TC+CC model (B).

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Supplementary Figure 2. Begg’s funnel plot analysis was used to detect publication bias for the association be-tween PON-1 -108Q/R polymorphism and PCOS risk in RR+RQ vs. QQ model.

Supplementary Figure 3. Begg’s funnel plot analysis was used to detect publication bias for the association be-tween PON-1 -1031T/C polymorphism and PCOS risk in CC+CT vs. TT model.